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Auto-regressive large language models (LLMs) have yielded impressive performance in many real-world tasks.
Interpolated estimation of markov source parameters from sparse data
Frederick Jelinek · 1980
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A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin · 2003
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Generating sequences with recurrent neural networks
Alex Graves · 2013
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2016
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2019
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Intrinsic dimensionality explains the effectiveness of language model fine-tuning
Armen Aghajanyan, Sonal Gupta, and Luke Zettlemoyer · 2021
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
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Gradient-based adversarial attacks against text transformers
Chuan Guo, Alexandre Sablayrolles, Hervé Jégou, and Douwe Kiela · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang · 2021
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Sponge examples: Energy-latency attacks on neural networks
Ilia Shumailov, Yiren Zhao, Daniel Bates, Nicolas Papernot, Robert Mullins, and Ross Anderson · 2021
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Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale
Reza Yazdani Aminabadi, Samyam Rajbhandari, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Olatunji Ruwase, Shaden Smith, Minjia Zhang, Jeff Rasley, et al · 2022
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Nmtsloth: understanding and testing efficiency degradation of neural machine translation systems
Simin Chen, Cong Liu, Mirazul Haque, Zihe Song, and Wei Yang · 2022
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Glm: General language model pretraining with autoregressive blank infilling
Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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The carbon footprint of machine learning training will plateau, then shrink
David Patterson, Joseph Gonzalez, Urs Hölzle, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David R So, Maud Texier, and Jeff Dean · 2022
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Denial-of-service attack on object detection model using universal adversarial perturbation
Avishag Shapira, Alon Zolfi, Luca Demetrio, Battista Biggio, and Asaf Shabtai · 2022
In chatgpt we trust? measuring and characterizing the reliability of chatgpt
Xinyue Shen, Zeyuan Chen, Michael Backes, and Yang Zhang · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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BloombergGPT: A large language model for finance
Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann · 2023
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
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A contrastive framework for neural text generation
Yixuan Su, Tian Lan, Yan Wang, Dani Yogatama, Lingpeng Kong, and Nigel Collier · 2022
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Self-instruct: Aligning language model with self generated instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh Hajishirzi · 2022
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Scaling transformer to 1M tokens and beyond with RMT
Aydar Bulatov, Yuri Kuratov, and Mikhail S Burtsev · 2023
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Longnet: Scaling transformers to 1,000,000,000 tokens
Jiayu Ding, Shuming Ma, Li Dong, Xingxing Zhang, Shaohan Huang, Wenhui Wang, Nanning Zheng, and Furu Wei · 2023
Cited alongside, same era.
Unlocking the power of generative AI models and systems such as GPT-4 and ChatGPT for higher education: A guide for students and lecturers
Henner Gimpel, Kristina Hall, Stefan Decker, Torsten Eymann, Luis Lämmermann, Alexander Mädche, Maximilian Röglinger, Caroline Ruiner, Manfred Schoch, Mareike Schoop, et al · 2023
Cited alongside, same era.
Platypus: Quick, cheap, and powerful refinement of LLMs
Ariel Lee, Cole Hunter, and Nataniel Ruiz · 2023
Cited alongside, same era.
Slowlidar: Increasing the latency of lidar-based detection using adversarial examples
Han Liu, Yuhao Wu, Zhiyuan Yu, Yevgeniy Vorobeychik, and Ning Zhang · 2023
Cited alongside, same era.
Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, et al · 2023
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Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J Zico Kolter, and Matt Fredrikson · 2023
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Llmeffichecker: Understanding and testing efficiency degradation of large language models
Xiaoning Feng, Xiaohong Han, Simin Chen, and Wei Yang · 2024
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Inducing high energy-latency of large vision-language models with verbose images
Kuofeng Gao, Yang Bai, Jindong Gu, Shu-Tao Xia, Philip Torr, Zhifeng Li, and Wei Liu · 2024
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Coercing llms to do and reveal (almost) anything
Jonas Geiping, Alex Stein, Manli Shu, Khalid Saifullah, Yuxin Wen, and Tom Goldstein · 2024
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LLM inference serving: Survey of recent advances and opportunities
Baolin Li, Yankai Jiang, Vijay Gadepally, and Devesh Tiwari · 2024
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Slowtrack: Increasing the latency of camera-based perception in autonomous driving using adversarial examples
Chen Ma, Ningfei Wang, Qi Alfred Chen, and Chao Shen · 2024
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The impact of uniform inputs on activation sparsity and energy-latency attacks in computer vision
Andreas Müller and Erwin Quiring · 2024
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Splitwise: Efficient generative llm inference using phase splitting
Pratyush Patel, Esha Choukse, Chaojie Zhang, Aashaka Shah, Íñigo Goiri, Saeed Maleki, and Ricardo Bianchini · 2024
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Beyond phantomsponges: Enhancing sponge attack on object detection models
Coen Schoof, Stefanos Koffas, Mauro Conti, and Stjepan Picek · 2024
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Sponge backdoor attack: Increasing the latency of object detection exploiting non-maximum suppression
Yong Xiao, Jin Ma, Ping Yi, and Xiuzhen Chen · 2024
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Promptcare: Prompt copyright protection by watermark injection and verification
Hongwei Yao, Jian Lou, Zhan Qin, and Kui Ren · 2024
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